Policy Optimization

Policy optimization in RL is the process of learning the optimal policy, which is the strategy that an agent follows to select actions in an environment to maximize the expected return.

Policy optimization algorithms are designed to learn the optimal policy by interacting with the environment, observing the rewards and transitions, and updating the policy based on the observed (or expected) rewards. Policy optimization algorithms can be classified into two main categories: value-based and policy-based.